Asked Resolve AI map Kafka cluster architecture by analyzing infrastructure configurations, tracing producers and consumers through source code, visualizing message flows, checking production health across dashboards and metrics, and identifying security vulnerabilities with prioritized remediation steps.
What makes this hard?
Your Kubernetes dashboard shows pod status and resource limits, but doesn't explain what services actually produce or consume from Kafka. GitHub shows you that three services import Kafka libraries, but not which topics they use or how they're configured. Grafana shows latency spikes, but doesn't connect them to specific services or code patterns.
Understanding Kafka architecture requires disconnected work across multiple tools:
- Read deployment YAMLs to find Kafka pods, memory limits, and service configurations
- Search codebase for Kafka client usage across multiple languages (Go, C#, Kotlin)
- Manually trace topic names, consumer groups, and producer configurations in code
- Check dashboards for service health metrics and error rates over time
- Query infrastructure metrics separately for CPU, memory, and network utilization
- Manually connect: deployment specs → code implementations → topic flows → production health → security configurations
How did Resolve AI help?
With one query, Resolve AI simultaneously analyzed Kubernetes infrastructure and source code across three languages to map the complete Kafka ecosystem:
- Identified producer with code evidence: Checkout service publishes OrderResult protobuf to orders topic from src/checkout/kafka/producer.go
- Found both consumers with configurations: Accounting service and Fraud Detection both consuming from orders topic, with init containers waiting for Kafka health
- Mapped cluster infrastructure: Single Kafka broker with plaintext protocol
- Analyzed production health from dashboards: Discovered critical issues: checkout service showing 15,000ms P99 latency spikes with recurring daily pattern, 100% error rate spike at specific timestamp, frontend outage starting at 08:15 UTC
- Identified security vulnerabilities across code and infrastructure: No SASL authentication on any service, insecure gRPC, hardcoded JWT key, missing security contexts on all three deployments
- Detected monitoring gaps: No Kafka-specific infrastructure metrics available, all CPU/memory/network queries timing out, preventing full resource utilization assessment
Resolve AI connected code-level implementation details to infrastructure configuration to production symptoms to security posture. Every finding included file citations and specific line numbers, creating a complete operational picture from a single question
